Community‐wide validation of geospace model ground magnetic field perturbation predictions to support model transition to operations

TLDR

Ground magnetic field fluctuations, closely tied to geomagnetically induced currents, are studied in a community‑wide validation effort coordinated by CCMC, SWPC, and modelers. The study aims to support NOAA SWPC in selecting the next operational space‑weather model by extending community‑wide validation of ground magnetic field predictions. The authors validated six geomagnetic events at 12 observatories, building event‑based contingency tables and computing POD, POFD, and HSS to quantify model performance. The validation reports POD, POFD, and HSS metrics, and an online CCMC interface enables detailed time‑series analysis.

Abstract

In this paper we continue the community‐wide rigorous modern space weather model validation efforts carried out within GEM, CEDAR and SHINE programs. In this particular effort, in coordination among the Community Coordinated Modeling Center (CCMC), NOAA Space Weather Prediction Center (SWPC), modelers, and science community, we focus on studying the models' capability to reproduce observed ground magnetic field fluctuations, which are closely related to geomagnetically induced current phenomenon. One of the primary motivations of the work is to support NOAA SWPC in their selection of the next numerical model that will be transitioned into operations. Six geomagnetic events and 12 geomagnetic observatories were selected for validation. While modeled and observed magnetic field time series are available for all 12 stations, the primary metrics analysis is based on six stations that were selected to represent the high‐latitude and mid‐latitude locations. Events‐based analysis and the corresponding contingency tables were built for each event and each station. The elements in the contingency table were then used to calculate Probability of Detection (POD), Probability of False Detection (POFD) and Heidke Skill Score (HSS) for rigorous quantification of the models' performance. In this paper the summary results of the metrics analyses are reported in terms of POD, POFD and HSS. More detailed analyses can be carried out using the event by event contingency tables provided as an online appendix. An online interface built at CCMC and described in the supporting information is also available for more detailed time series analyses.